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Abstract Few studies have utilized machine learning techniques to predict or understand the Madden‐Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here, we present a simple framework for real‐time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real‐time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ∼18 days in October‐March and ∼11 days in April‐September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks are highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena. 

Abstract The Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment conducted a field campaign inAugust-October 2018. The R/V Thomas G. Thompson made two cruises in thewestern North Pacific region north of Palau and east of the Philippines. Using select field observations and global observational and reanalysis data sets, this study describes the large-scale state and evolution of the atmosphere and ocean during these cruises. Intraseasonal variability was weak during the field program, except for a period of suppressed convection in October. Tropical cyclone activity, on the other hand, was strong. Variability at the ship location was characterized by periods of low-level easterly atmospheric flow with embedded westward propagating synoptic-scale atmospheric disturbances, punctuated by periods of strong low-level westerly winds that were both connected to the Asian monsoon westerlies and associated with tropical cyclones. In the most dramatic case, westerlies persisted for days during and after tropical cyclone Jebi had passed to the north of the ship. In these periods, the sea surface temperature was reduced by a couple of degrees by both wind mixing and net surface heat fluxes that were strongly (~200 Wm −2 ) out of the ocean, due to both large latent heat flux and cloud shading associated with widespread deep convection. Underway conductivity-temperature transects showed dramatic cooling and deepening of the ocean mixed layer and erosion of the barrier layer after the passage of Typhoon Mangkhut due to entrainment of cooler water from below. Strong zonal currents observed over at least the upper 400 meters were likely related to the generation and propagation of near-inertial currents. 

Abstract Observational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection. 

Abstract The stratospheric quasi-biennial oscillation (QBO) induces temperature anomalies in the lower stratosphere and tropical tropopause layer (TTL) that are cold when lower-stratospheric winds are easterly and warm when winds are westerly. Recent literature has indicated that these QBO temperature anomalies are potentially important in influencing the tropical troposphere, and particularly in explaining the relationship between the QBO and the Madden–Julian oscillation (MJO). The authors examine the variability of QBO temperature anomalies across several time scales using reanalysis and observational datasets. The authors find that, in boreal winter relative to other seasons, QBO temperature anomalies are significantly stronger (i.e., colder in the easterly phase of the QBO and warmer in the westerly phase of the QBO) on the equator, but weaker off the equator. The equatorial and subtropical changes compensate such that meridional temperature gradients and thus (by thermal wind balance) equatorial zonal wind anomalies do not vary in amplitude as the temperature anomalies do. The same pattern of stronger on-equatorial and weaker off-equatorial QBO temperature anomalies is found on decadal time scales: stronger anomalies are seen for 1999–2019 compared to 1979–99. The causes of these changes to QBO temperature anomalies, as well as their possible relevance to the MJO–QBO relationship, are not known.